Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma

Kawazoe, Yusuke; Shiinoki, Takehiro; Fujimoto, Kiyoshige; Yuasa, Yuki; Hirano, Tsunahiko; Matsunaga, Kazuto; Tanaka, Hidekazu

doi:10.1002/acm2.13980

Cited by 6 publications

(2 citation statements)

References 47 publications

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“…A recent study compared radiomic features of multiple peritumoral regions (3 mm, 5 mm, 7 mm) and constructed three machine learning models to predict EGFR mutation status in NSCLC. The results showed that combining intratumoral and peritumoral 3 mm radiomic features could better distinguish EGFR+ from EGFR− groups than 5 mm and 7 mm (training, p = 0.0000, test, p = 0.0025), but this study included only 164 patients and did not validate models with an external dataset [23]. Based on this, we expanded VOI_I outwards by 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 10 mm, and 15 mm to identify seven peritumoral regions and combined them with intratumoral regions to generate seven intratumoral and peritumoral regions, respectively, to compare the complementary value of different peritumoral regions to the predictive performance of radiomic models.…”

Section: Discussionmentioning

confidence: 94%

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

Shang,

Chen,

et al. 2023

Radiol med

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Objective To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. Materials and methods A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC). Results 399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR−). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively). Conclusions Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.

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Section: Discussionmentioning

confidence: 94%

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

Shang,

Chen,

et al. 2023

Radiol med

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“…Currently, several studies revealed that the radiomics model of the +3 mm peritumoral area effectively predicted chemotherapy response or EGFR mutation in non-small-cell lung cancer ( 19 , 29 , 30 ). This study of differentiating LPA and Non-LPA lung cancer also obtained the same peritumoral area (the PTV 0~+3 model had the best AUC in peritumoral models).…”

Section: Discussionmentioning

confidence: 99%

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes

Zhou,

Yang,

Xiong

et al. 2024

Transl Cancer Res

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Background The identification of different subtypes of early-stage lung invasive adenocarcinoma before surgery contributes to the precision treatment. Radiomics could be one of the effective and noninvasive identification methods. The value of peritumoral radiomics in predicting the subtypes of early-stage lung invasive adenocarcinoma perhaps clinically useful. Methods This retrospective study included 937 lung adenocarcinomas which were randomly divided into the training set (n=655) and testing set (n=282) with a ratio of 7:3. This study used the univariate and multivariate analysis to choose independent clinical predictors. Radiomics features were extracted from 18 regions of interest (1 intratumoral region and 17 peritumoral regions). Independent and conjoint prediction models were constructed based on radiomics and clinical features. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE). Significant differences between areas under the ROC (AUCs) were estimated using in the Delong test. Results Patient age, smoking history, carcinoembryonic antigen (CEA), lesion location, length, width and clinic behavior were the independent predictors of differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. The highest AUC value among the 19 independent models was obtained for the PTV 0~+3 radiomics model with 0.849 for the training set and 0.854 for the testing set. As the peritumoral distance increased, the predictive power of the models decreased. The radiomics-clinical conjoint model was statistically significantly different from the other models in the Delong test (P<0.05). Conclusions The intratumoral and peritumoral regions contained a wealth of clinical information. The diagnostic efficacy of intra-peritumoral radiomics combined clinical model was further improved, which was particularly important for preoperative staging and treatment decision-making.

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Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma

Kawazoe

Shiinoki

Fujimoto

et al. 2023

J Applied Clin Med Phys

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Purpose We investigated optimal peritumoral size and constructed predictive models for epidermal growth factor receptor (EGFR) mutation. Methods A total of 164 patients with lung adenocarcinoma were retrospectively analyzed. Radiomic signatures for the intratumoral region and combinations of intratumoral and peritumoral regions (3, 5, and 7 mm) from computed tomography images were extracted using analysis of variance and least absolute shrinkage. The optimal peritumoral region was determined by radiomics score (rad‐score). Intratumoral radiomic signatures with clinical features (IRS) were used to construct predictive models for EGFR mutation. Combinations of intratumoral and 3, 5, or 7 mm‐peritumoral signatures with clinical features (IPRS3, IPRS5, and IPRS7, respectively) were also used to construct predictive models. Support vector machine (SVM), logistic regression (LR), and LightGBM models with five‐fold cross‐validation were constructed, and the receiver operating characteristics were evaluated. Area under the curve (AUC) of the training and test cohorts values were calculated. Brier scores (BS) and decision curve analysis (DCA) were used to evaluate the predictive models. Results The AUC values of the SVM, LR, and LightGBM models derived from IRS were 0.783 (95% confidence interval: 0.602–0.956), 0.789 (0.654–0.927), and 0.735 (0.613–0.958) for training, and 0.791 (0.641–0.920), 0.781 (0.538–0.930), and 0.734 (0.538–0.930) for test cohort, respectively. Rad‐score confirmed that the 3 mm‐peritumoral size was optimal (IPRS3), and AUCs values of SVM, LR, and lightGBM models derived from IPRS3 were 0.831 (0.666–0.984), 0.804 (0.622–0.908), and 0.769 (0.628–0.921) for training and 0.765 (0.644–0.921), 0.783 (0.583–0.921), and 0.796 (0.583–0.949) for test cohort, respectively. The BS and DCA of the LR and LightGBM models derived from IPRS3 were better than those from IRS. Conclusion Accordingly, the combination of intratumoral and 3 mm‐peritumoral radiomic signatures may be helpful for predicting EGFR mutations.

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Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma

Cited by 6 publications

References 47 publications

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes

Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma

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